Molecular cloning of the cDNA encoding pp36, a tyrosine-phosphorylated adaptor protein selectively expressed by T cells and natural killer cells.

Activation of T and natural killer (NK) cells leads to the tyrosine phosphorylation of pp36 and to its association with several signaling molecules, including phospholipase Cgamma-1 and Grb2. Microsequencing of peptides derived from purified rat pp36 protein led to the cloning, in rat and man, of cDNA encoding a T- and NK cell-specific protein with several putative Src homology 2 domain-binding motifs. A rabbit antiserum directed against a peptide sequence from the cloned rat molecule recognized tyrosine phosphorylated pp36 from pervanadate-treated rat thymocytes. When expressed in 293T human fibroblast cells and tyrosine-phosphorylated, pp36 associated with phospholipase Cgamma-1 and Grb2. Studies with GST-Grb2 fusion proteins demonstrated that the association was specific for the Src homology 2 domain of Grb-2. Molecular cloning of the gene encoding pp36 should facilitate studies examining the role of this adaptor protein in proximal signaling events during T and NK cell activation

Ear-clipping Based Algorithms of Generating High-quality Polygon Triangulation

A basic and an improved ear clipping based algorithm for triangulating simple polygons and polygons with holes are presented. In the basic version, the ear with smallest interior angle is always selected to be cut in order to create fewer sliver triangles. To reduce sliver triangles in further, a bound of angle is set to determine whether a newly formed triangle has sharp angles, and edge swapping is accepted when the triangle is sharp. To apply the two algorithms on polygons with holes, "Bridge" edges are created to transform a polygon with holes to a degenerate polygon which can be triangulated by the two algorithms. Applications show that the basic algorithm can avoid creating sliver triangles and obtain better triangulations than the traditional ear clipping algorithm, and the improved algorithm can in further reduce sliver triangles effectively. Both of the algorithms run in O(n2) time and O(n) space.Comment: Proceedings of the 2012 International Conference on Information Technology and Software Engineering Lecture Notes in Electrical Engineering Volume 212, 2013, pp 979-98

Exploring contrast generalisation in deep learning-based brain MRI-to-CT synthesis

Background: Synthetic computed tomography (sCT) has been proposed and increasingly clinically adopted to enable magnetic resonance imaging (MRI)-based radiotherapy. Deep learning (DL) has recently demonstrated the ability to generate accurate sCT from fixed MRI acquisitions. However, MRI protocols may change over time or differ between centres resulting in low-quality sCT due to poor model generalisation. Purpose: investigating domain randomisation (DR) to increase the generalisation of a DL model for brain sCT generation. Methods: CT and corresponding T1-weighted MRI with/without contrast, T2-weighted, and FLAIR MRI from 95 patients undergoing RT were collected, considering FLAIR the unseen sequence where to investigate generalisation. A ``Baseline'' generative adversarial network was trained with/without the FLAIR sequence to test how a model performs without DR. Image similarity and accuracy of sCT-based dose plans were assessed against CT to select the best-performing DR approach against the Baseline. Results: The Baseline model had the poorest performance on FLAIR, with mean absolute error (MAE)=106$\pm$20.7 HU (mean$\pm\sigma$). Performance on FLAIR significantly improved for the DR model with MAE=99.0$\pm$14.9 HU, but still inferior to the performance of the Baseline+FLAIR model (MAE=72.6$\pm$10.1 HU). Similarly, an improvement in $\gamma$-pass rate was obtained for DR vs Baseline. Conclusions: DR improved image similarity and dose accuracy on the unseen sequence compared to training only on acquired MRI. DR makes the model more robust, reducing the need for re-training when applying a model on sequences unseen and unavailable for retraining.Comment: Preprint submitted to Physica Medica on 2023-02-16 for review. Also published in Zenodo at https://doi.org/10.5281/zenodo.774264

Trapped lipopolysaccharide and LptD intermediates reveal lipopolysaccharide translocation steps across the Escherichia coli outer membrane

Lipopolysaccharide (LPS) is a main component of the outer membrane of Gram-negative bacteria, which is essential for the vitality of most Gram-negative bacteria and plays a critical role for drug resistance. LptD/E complex forms a N-terminal LPS transport slide, a hydrophobic intramembrane hole and the hydrophilic channel of the barrel, for LPS transport, lipid A insertion and core oligosaccharide and O-antigen polysaccharide translocation, respectively. However, there is no direct evidence to confirm that LptD/E transports LPS from the periplasm to the external leaflet of the outer membrane. By replacing LptD residues with an unnatural amino acid p-benzoyl-L-phenyalanine (pBPA) and UV-photo-cross-linking in E.coli, the translocon and LPS intermediates were obtained at the N-terminal domain, the intramembrane hole, the lumenal gate, the lumen of LptD channel, and the extracellular loop 1 and 4, providing the first direct evidence and “snapshots” to reveal LPS translocation steps across the outer membrane

MRI in multiple myeloma : a pictorial review of diagnostic and post-treatment findings

Magnetic resonance imaging (MRI) is increasingly being used in the diagnostic work-up of patients with multiple myeloma. Since 2014, MRI findings are included in the new diagnostic criteria proposed by the International Myeloma Working Group. Patients with smouldering myeloma presenting with more than one unequivocal focal lesion in the bone marrow on MRI are considered having symptomatic myeloma requiring treatment, regardless of the presence of lytic bone lesions. However, bone marrow evaluation with MRI offers more than only morphological information regarding the detection of focal lesions in patients with MM. The overall performance of MRI is enhanced by applying dynamic contrast-enhanced MRI and diffusion weighted imaging sequences, providing additional functional information on bone marrow vascularization and cellularity. This pictorial review provides an overview of the most important imaging findings in patients with monoclonal gammopathy of undetermined significance, smouldering myeloma and multiple myeloma, by performing a 'total' MRI investigation with implications for the diagnosis, staging and response assessment. Main message aEuro cent Conventional MRI diagnoses multiple myeloma by assessing the infiltration pattern. aEuro cent Dynamic contrast-enhanced MRI diagnoses multiple myeloma by assessing vascularization and perfusion. aEuro cent Diffusion weighted imaging evaluates bone marrow composition and cellularity in multiple myeloma. aEuro cent Combined morphological and functional MRI provides optimal bone marrow assessment for staging. aEuro cent Combined morphological and functional MRI is of considerable value in treatment follow-up

Does native Trypanosoma cruzi calreticulin mediate growth inhibition of a mammary tumor during infection?

Indexación: Web of Science.Background: For several decades now an antagonism between Trypanosoma cruzi infection and tumor development has been detected. The molecular basis of this phenomenon remained basically unknown until our proposal that T. cruzi Calreticulin (TcCRT), an endoplasmic reticulum-resident chaperone, translocated-externalized by the parasite, may mediate at least an important part of this effect. Thus, recombinant TcCRT (rTcCRT) has important in vivo antiangiogenic and antitumor activities. However, the relevant question whether the in vivo antitumor effect of T. cruzi infection is indeed mediated by the native chaperone (nTcCRT), remains open. Herein, by using specific modified anti-rTcCRT antibodies (Abs), we have neutralized the antitumor activity of T. cruzi infection and extracts thereof, thus identifying nTcCRT as a valid mediator of this effect. Methods: Polyclonal anti-rTcCRT F(ab')(2) Ab fragments were used to reverse the capacity of rTcCRT to inhibit EAhy926 endothelial cell (EC) proliferation, as detected by BrdU uptake. Using these F(ab')(2) fragments, we also challenged the capacity of nTcCRT, during T. cruzi infection, to inhibit the growth of an aggressive mammary adenocarcinoma cell line (TA3-MTXR) in mice. Moreover, we determined the capacity of anti-rTcCRT Abs to reverse the antitumor effect of an epimastigote extract (EE). Finally, the effects of these treatments on tumor histology were evaluated. Results: The rTcCRT capacity to inhibit ECs proliferation was reversed by anti-rTcCRT F(ab')(2) Ab fragments, thus defining them as valid probes to interfere in vivo with this important TcCRT function. Consequently, during infection, these Ab fragments also reversed the in vivo experimental mammary tumor growth. Moreover, anti-rTcCRT Abs also neutralized the antitumor effect of an EE, again identifying the chaperone protein as an important mediator of this anti mammary tumor effect. Finally, as determined by conventional histological parameters, in infected animals and in those treated with EE, less invasive tumors were observed while, as expected, treatment with F(ab')(2) Ab fragments increased malignancy. Conclusion: We have identified translocated/externalized nTcCRT as responsible for at least an important part of the anti mammary tumor effect of the chaperone observed during experimental infections with T. cruzi.http://bmccancer.biomedcentral.com/articles/10.1186/s12885-016-2764-

On the Schoenberg Transformations in Data Analysis: Theory and Illustrations

The class of Schoenberg transformations, embedding Euclidean distances into higher dimensional Euclidean spaces, is presented, and derived from theorems on positive definite and conditionally negative definite matrices. Original results on the arc lengths, angles and curvature of the transformations are proposed, and visualized on artificial data sets by classical multidimensional scaling. A simple distance-based discriminant algorithm illustrates the theory, intimately connected to the Gaussian kernels of Machine Learning